Top Artificial Intelligence Jobs in Italy

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Provide EU-focused counsel on AI and privacy regulation (EU AI Act, GDPR, DSA); draft policies, DPA and contractual terms; translate legal requirements into technical and operational controls; advise product, engineering, and go-to-market teams; liaise with regulators and manage compliance programs and risk assessments.

As a Customer Support Expert, you'll assist brands by offering customer support via social media and email, responding to inquiries, and ensuring positive customer experiences.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

No responsibilities or duties were listed in the job description.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.

Lead strategic AI adoption for enterprise CX: discover use cases, design reference architectures, deliver rapid POCs/MVPs, integrate AI with CRM/ERP, build evaluation pipelines, and mentor teams to scale responsible, outcome-oriented agentic AI solutions.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Participate in a 14-day remote pilot using a qualifying Google Pixel device to interact naturally with notification-generating apps, complete daily 10-15 minute notification sorting tasks, submit daily feedback and documentation, and report technical issues. Must follow instructions and maintain confidentiality via the Upwork platform.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Build end-to-end AI features and prototypes across backend and frontend, prototype AI agent use cases, create UI/blueprints/examples to make AI workflows usable, and ship features quickly in collaboration with product and backend teams.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Lead end-to-end implementation of QVAC-powered AI deployments, acting as primary client contact, coordinating cross-functional teams, defining roadmaps, managing risks, and ensuring production rollouts while aligning product capabilities with client objectives and documenting reusable architectures and lessons learned.

Design and implement AI solutions using ML models and NLP, collaborate with teams, and support business process improvement through AI.

Build and integrate LLM and generative AI features into Hume Maestro. Design agentic workflows (tool use, reflection, prompt chaining), implement production-grade model serving and CI/CD, enable engineers through mentoring and best practices, and support deployment, monitoring, and evaluation of AI components.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Develop, design, test and implement imaging and 3D reconstruction algorithms using C++ and Python. Analyze real data, optimize for high-performance computing, integrate algorithms into applications, and collaborate across algorithms, physics, and electronics teams while participating in design and code reviews.

As an AI Senior Business Consultant, you will leverage AI technologies to drive strategic business opportunities, analyzing trends, and facilitating AI-driven transformations across multiple industries.

Senior individual contributor who architects, prototypes, and ships production-grade AI solutions for Finance. Focus areas include agentic AI, multi-agent orchestration, ML forecasting and anomaly detection, RAG and vector-store solutions, responsible AI and governance, and embedding AI into FP&A processes. Partners with engineering, IT, security, and finance teams to productionize systems and establish standards, monitoring, and controls.

Design, build and deploy AI solutions (custom copilots, conversational agents, embedded AI) using Microsoft Copilot Studio and Azure AI Foundry. Integrate AI with Power Automate, Snowflake and enterprise systems, collaborate with stakeholders, operationalise models, monitor performance, and champion responsible AI practices.

The role involves designing AI solutions, developing machine learning models, collaborating with clients, and participating in presales activities.